Application of neural networks in photovoltaic conversion systems

In the recent years, due to global warming solar panels are becoming popular because of their non-polluting operation and ability to convert solar irradiation directly into electricity. Solar panels however have low efficiency, which is also influenced by the change of environmental conditions, such as irradiance and temperature. Therefore, to maximize the efficiency, maximum power point tracking (MPPT) algorithms are used. The available algorithms differ in complexity, robustness and efficiency. The classical algorithms are simple and working appropriately under steady conditions, but can loose the maximum power point under rapidly changing solar irradiance or when the panel is only partially shaded. To solve these problems, new methods are developed, such as Fuzzy MPPT or artificial neural network (ANN) based MPPT which can perform better under these conditions. ANN has several advantages which can be used in engineering applications, they can be trained off-line, they are robust and can solve non-linear problems, which makes them suitable for MPPT application.

This study examines the application of neural networks in MPPT algorithms and compares the results with other MPPT methods. The examined photovoltaic conversion system consists of the PV panel (36 series-connected PV cells), the Buck/Boost DC/DC converter and the resistive load. Several different MPPT algorithms are examined, such as Perturb and Observe (P&O), Incremental Conductance (InCond), Fuzzy MPPT, Adaptive-Network-Based Inference System (ANFIS) based MPPT and ANN based MPPT. As this study focuses mainly on the neural networks, their behaviour is detailed and different training algorithms are examined, such as stochastic gradient descent, Levenberg-Marquardt and Neuron-by-Neuron. The training algorithms are implemented in MATLAB and compared in function approximation application using the same example function. The MPPT algorithms are at first simulated in MATLAB/Simulink environment, then they are tested in Hardware-In-The-Loop (HiL) simulations as well. It can be stated that in both cases the ANN based MPPT gave the best results, however if the system parameters differ from the nominal values, the ANN looses efficiency and even the classical algorithms can perform better. The effect of partial shading is also examined through simulations, from the results it can be observed that if the ANN is trained using partially shaded data, it can outperform all examined MPPT algorithms.